# How do I analyse data with 2 independent variables and 2 dependent variables?

Im confused about what to do. I was thinking of running two seperate multiple regressions with a DV in each.. its after this, that I'm stuck. How do I see what effect the two DV's have combined? Or am I going about the whole thing wrong. Help!

My IV's are gender and group.

My DV's will be scores on two seperate psychometric tests (likert scales)

I hope to have at least 100 people in each group (3 groups) so sample size will be roughly 300.

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+1 for thinking about this before collecting data. Good on you! I already answered that MANOVA (en.wikipedia.org/wiki/Multivariate_analysis_of_variance) might be the way to go, but this presupposes normally distributed residuals, which your Likert scales will not provide, so I deleted my non-answer right away... – Stephan Kolassa Nov 29 '12 at 18:01
Thanks anyway Stephan, any help is appreciated!! :) – karen murray Nov 29 '12 at 18:06
Googling for "alternative to MANOVA for ordinal data" did not turn up a lot of helpful things, except for perhaps this: smj.sagepub.com/content/7/1/3.abstract You may want to look into that paper, perhaps there are a few pointers there. – Stephan Kolassa Nov 29 '12 at 18:07
I'll have a look now, thanks a mill! – karen murray Nov 29 '12 at 18:12
Perhaps if you think through what you mean by "the effect the two DVs have combined" it will help decide if you need to worry about this or you can just run to separate regressions. Generally you aren't thinking about the "effect" of a DV. So in what way is it important to look at the two of them simultaneously? This will depend on your research question. – Peter Ellis Nov 29 '12 at 18:47

Are you interested in examining the correlation between the dependent variables in the same model? I can't speak to the multiple independent variables part of the question, but you could investigate using a linear mixed model with multiple response variables (if your data will be longitudinal). I don't know of a website (I'm sure there is stuff out there, I just don't have a reference), but the book by Jeffrey Long, 'Longitudinal data analysis for the behavioral sciences using R' may be of use. Chapter 13 (p 501) has a section on models with multiple dv's.

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